Search NASA⌕ Search

SEARCH · Search NASA

Results for “Spatial”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Challenges in spatial metabolomics and proteomics for functional tissue unit and single-cell resolution

While transcriptomics is the most broadly applied technology for global spatial and single cell measurements in healthy and diseased tissues. Transcripts are often used as a proxy for protein and even metabolite measurements, but it has become commonly accepted that extrapolating this kind of information is a poor proxy and not a substitute for direct measurement. Within the last decade advanced developments of mass spectrometry-based assays have made these direct measurements not only possible, but routine. Where mass spectrometry has become an enabling technology, and various methods can now detect hundreds of metabolites and thousands of proteins from samples. Not only can this be performed within bulk measurements, but much effort has been directed into translating these measurements to single cells and tissues at cellular resolution. The information obtained from mass spectrometry is now able to trace metabolic events and decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein, we will broadly overview developments in the field over the past decade, leading into several case studies which highlight the direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspective on paths forward and the challenges that persist with adoption and application of spatial omics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Unraveling plant–microbe symbioses using single-cell and spatial transcriptomics

Plant-microbe symbioses require intense interaction and genetic coordination to successfully establish in specific cell types of the host and symbiont. Traditional RNA-seq methodologies lack the cellular resolution to fully capture these complexities, but single-cell and spatial transcriptomics (ST) are now allowing scientists to probe symbiotic interactions at an unprecedented level of detail. Here, we discuss the advantages that novel spatial and single-cell transcriptomic technologies provide in studying plant-microbe endosymbioses and highlight key recent studies. Finally, we consider the remaining limitations of applying these approaches to symbiosis research, which are mainly related to the simultaneous capture of both plant and microbial transcripts within the same cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spatial profile of argon (1s 5 ) metastables in an electron beam generated plasma

Electron beams with an applied magnetic field generate a secondary cold plasma with a selective chemical composition, featuring low-energy ions and metastable species in the discharge periphery, ideal for low-damage plasma treatment of material substrates. In this work, we studied the plasma generated by an e-beam using a 4 kV voltage in a pure argon gas environment under a magnetic field of 150 G and in the pressure range of 25–90 mTorr. We measured the absolute spatial density profile of argon (1 s 5 ) metastables in an electron beam generated plasma by laser-induced fluorescence and found it to be of the order of 10 16 m −3 . The electron temperature and the electron density measured by a Langmuir probe were of the order of 10 16 m −3 and less than an eV respectively. Electron-impact quenching was identified as a significant loss mechanism for the Ar(1s 5 ) state, leading to the saturation of the metastable density at higher pressures. Outside the primary ionization region, the spatial distribution of argon metastables followed a linear diffusion profile, indicating negligible additional production in those regions.

EEDF↗
DOE OSTI2026

Propagation of partially spatially coherent laser beams in instantaneous Kerr media

The propagation of intense, partially spatially coherent laser beams in a medium with instantaneous third-order susceptibility is studied analytically and numerically. For sufficiently high power relative to that required for nonlinear self-focusing, the propagation initially proceeds in two stages. In the first stage, spatial coherence builds up, and in the second stage, the number of speckles reduces. Once the degree of coherence is sufficiently high, whole-beam self-focusing occurs. The beam power is mostly confined within the initial spot radius. Two analytical approaches for describing the evolution of the beam are presented. The method of moments leads to an analytical solution for the rms spot radius that is in excellent agreement with simulations. This method does not require any knowledge of the field statistics beyond the initial conditions and provides no information about the evolution of the individual speckles. The other approach employs a self-similar solution for the second-order coherence function of the field and assumes that the fourth-order coherence function is factorizable and obeys complex circular Gaussian random statistics. The latter method also leads to an analytical expression for the spot radius, but its predictions for the qualitative evolution of the speckles disagree with wave-optics simulations.

lasers↗
DOE OSTI2025

Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain-cropped land in the United States (US) as local markets developed for this high-yielding perennial grass (10–30 Mg DM ha −1 ). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing-derived vegetation indices to ground truth data from 105 hand-harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high-resolution satellite-sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha −1 ) and net profits ($\$$83 to $\$$1211.5 ha −1 ) was observed. All fields were profitable in all site-years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of $\$$130 Mg −1 varied from 9.0–12.1 Mg ha −1 at 15% moisture content (7.6–10.3 Mg DM ha −1 ). Breakeven prices ranged from $\$$73 to $\$$122.4 Mg −1 , matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial-scale M × g production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Comparison of theory with the experimental characterization of the spatial frequency response of interferometers using a binary pseudo-random array sample

Experimental evaluations of the surface height response of an interference microscope using a binary pseudo-random array test sample are compared with a theory based on a Fourier optics model. Measurements of key instrument characteristics, including the illumination, imaging, and obscuring apertures of three different Mirau objectives, support the theoretical calculations. Agreement between experimental and theoretical modeling confirms the predictability of the spatial frequency response for the purpose of specification and optimization of instrument configuration for specific metrology tasks. The results also provide confidence in methods of compensating for the decrease in instrument response with spatial frequency.

Calibration↗
DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗
NASA NTRS2002

Spatial learning and memory is preserved in rats after early development in a microgravity environment

This study evaluated the cognitive mapping abilities of rats that spent part of their early development in a microgravity environment. Litters of male and female Sprague-Dawley rat pups were launched into space aboard the National Aeronautics and Space Administration space shuttle Columbia on postnatal day 8 or 14 and remained in space for 16 days. These animals were designated as FLT groups. Two age-matched control groups remained on Earth: those in standard vivarium housing (VIV) and those in housing identical to that aboard the shuttle (AGC). On return to Earth, animals were tested in three different tasks that measure spatial learning ability, the Morris water maze (MWM), and a modified version of the radial arm maze (RAM). Animals were also tested in an open field apparatus to measure general activity and exploratory activity. Performance and search strategies were evaluated in each of these tasks using an automated tracking system. Despite the dramatic differences in early experience, there were remarkably few differences between the FLT groups and their Earth-bound controls in these tasks. FLT animals learned the MWM and RAM as quickly as did controls. Evaluation of search patterns suggested subtle differences in patterns of exploration and in the strategies used to solve the tasks during the first few days of testing, but these differences normalized rapidly. Together, these data suggest that development in an environment without gravity has minimal long-term impact on spatial learning and memory abilities. Any differences due to development in microgravity are quickly reversed after return to earth normal gravity.

NASA Discipline Neuroscience↗
NASA NTRS2013

The Spatial Extent and Distribution of Star Formation in 3D-HST Mergers at z is approximately 1.5

We present an analysis of the spatial distribution of star formation in a sample of 60 visually identified galaxy merger candidates at z greater than 1. Our sample, drawn from the 3D-HST survey, is flux-limited and was selected to have high star formation rates based on fits of their broad-band, low spatial resolution spectral energy distributions. It includes plausible pre-merger (close pairs) and post-merger (single objects with tidal features) systems,with total stellar masses and star formation rates derived from multi-wavelength photometry. Here we use near-infrared slitless spectra from 3D-HST which produce H or [OIII] emission line maps as proxies for star-formation maps. This provides a first comprehensive high-resolution, empirical picture of where star formation occurred in galaxy mergers at the epoch of peak cosmic star formation rate. We find that detectable star formation can occur in one or both galaxy centres, or in tidal tails. The most common case (58%) is that star formation is largely concentrated in a single, compact region, coincident with the centre of (one of) the merger components. No correlations between star formation morphology and redshift, total stellar mass, or star formation rate are found. A restricted set of hydrodynamical merger simulationsbetween similarly massive and gas-rich objects implies that star formation should be detectable in both merger components, when the gas fractions of the individual components are the same. This suggests that z is approximately 1.5 mergers typically occur between galaxies whose gas fractions, masses, andor star formation rates are distinctly different from one another.

Spatial↗
NASA NTRS2015

Experimental Estimation of CLASP Spatial and Spectral Resolutions: Results of the Instrument's Optical Alignment

The Chromospheric Lyman-Alpha SpectroPolarimeter is a sounding rocket experiment design to measure for the first time the polarization signal of the Lyman-Alpha line (121.6nm), emitted in the solar upper-chromosphere and transition region. This instrument aims to detect the Hanle effect's signature hidden in the Ly-alpha polarization, as a tool to probe the chromospheric magnetic field. Hence, an unprecedented polarization accuracy is needed ((is) less than 10 (exp -3). Nevertheless, spatial and spectral resolutions are also crucial to observe chhromospheric feature such as spicules, and to have precise measurement of the Ly-alpha line core and wings. Hence, this poster will present how the telescope and the spectrograph were separately aligned, and their combined spatial and spectral resolutions.

spatial resolution↗
NASA NTRS2018

An Assessment of the Differences Between Spatial Resolution and Grid Size for the SMAP Enhanced Soil Moisture Product over Homogeneous Sites

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled to a fixed Earth grid that facilitates their use in applications. In many cases the grid size is finer than the actual spatial resolution of the observation, and often this difference is not well understood by the user. Here, this issue was examined for the Soil Moisture Active Passive (SMAP) enhanced version of the passive-based soil moisture product, which has a grid size of 9-km and a nominal spatial resolution of 33-km. In situ observations from core validation sites were used to compute comparison metrics. For sites that satisfied the established reliability and scaling criteria, the impact of validating the 9-km grid product with in situ data collected over a 9-km versus a 33-km domain was very small for the sites studied (0.039 cu. m/cu. m unbiased root mean square difference for the 9-km case versus 0.037 cu. m/cu. m for the 33-km case). This result does not mean that the resolution of the product is 9-km but that for the conditions studied here the soil moisture estimated from in situ observations over 9-km is a close approximation of the soil moisture estimated from in situ observations over the 33-km resolution. The implication is that using the enhanced SMAP product at its grid resolution of 9-km should not introduce large errors in most applications.

Soil moisture↗
NASA NTRS2018

Mitigation of Attentional Tunneling in the Flight Deck Using a Spatial Auditory Display

The role of attentional bias to a specific form of information, e.g., visual versus aural instructions, was evaluated to address the issue of attentional fixation (i.e., "cognitive tunneling") in the flight deck. A method of mitigating attentional tunneling using a spatial auditory display was evaluated in the presence and absence of a biasing factor. Data were gathered for participant recognition, accuracy and response times to aural and visual instructions under different conditions of distractor stimuli. Results showed a significant main effect for target acquisition with the use of spatial audio techniques. No significant main effects or interactions were found for target acquisition timing. Target acquisition was not affected by a "gaming score" biasing factor associated with the distractor tasks.

readback errors↗
NASA NTRS2019

The Terrestrial Organism and Biogeochemistry Spatial Sampling Design for the National Ecological Observatory Network

The National Ecological Observatory Network (NEON) seeks to facilitate ecological prediction at a continental scale by measuring processes that drive change and responses at sites across the United States for thirty years. The spatial distribution of observations of terrestrial organisms and soil within NEON sites is determined according to a “design‐based” sample design that relies on the randomization of sampling locations. Development of the sample design was guided by high‐level NEON objectives and the multitude of data products that will be subjected to numerous analytical approaches to address the causes and consequences of ecological change. A requirement framework permeates the NEON design, ensuring traceability from each facet of the design to the high‐level requirements that make the NEON mission statement actionable. Requirements were developed for the terrestrial sample design to guide the key components of the design: Randomizing the sample locations ensures the unbiased collection of data, is appropriate for organisms and soil, and provides data suitable for a variety of analyses. Stratification increases efficiency and allows sampling to focus on those parts of the landscape measured by other NEON observation platforms. Attention to the sample size and spatial plot allocation ensures that data products will be sufficient to inform questions asked of the data and the NEON objectives. Establishing a framework with the capacity for re‐evaluate and design iteration allows for adaption to unexpected challenges and optimization of the sample design based on early data returns. The utility of the NEON sampling design is highlighted by its application across terrestrial systems. The data generated from this unique design will be used to quantify patterns in: the abundance and diversity of small mammals, breeding birds, insects, and soil microbes; vegetation structure, biomass, productivity, and diversity; and soil biogeochemistry.

National Ecological Observatory Network↗
NASA NTRS2023

Spatial Link Coverage Projections for the Glenn Research Center Communication Analysis Suite (GCAS)

Space communications are a fundamental part of every NASA mission, and reliable space communications are essential. Precise planning, simulation, and analysis of communications abilities are needed to know exactly what data rates, visibility times, and signal strength can be expected during the real missions. The goal of this work was to take a 3D icosphere, where each face was a point to be evaluated, and display the link performance onto a 2D projection of celestial bodies for links to a node location that could be spatially located at any of the icosahedral faces. 2D projections were produced for a wide range of spatial link grid sizes and compressed into low-size MAT files for ease of access and rapid implementation. The final icosahedral projections can be displayed onto various celestial body maps, using ultra-high-resolution maps for stitching. The results have proven satisfactory and are planned to support various simulation efforts.

communication link analysis↗
NASA NTRSDate not supplied

Development of an Improved Spatial Metadata Simplification Algorithm

The National Aeronautics and Space Administration's (NASA) Atmospheric Science Data Center (ASDC) at NASA Langley Research Center in Hampton, VA provides atmospheric science data products and services to the science community, including enhanced search and subsetting capabilities for numerous Earth Science datasets. The ASDC is the official Distributed Active Archive Center (DAAC) of record for the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. TEMPO is situated on a geostationary satellite positioned at a longitude near the center of the conterminous United States and focused on North America, making hourly swaths of its field of regard from east to west. Spatial metadata is an essential component for the discovery and distribution of Earth Science data. The simplified polygonal boundaries representing the archived data files ensure that any granule can be identified quickly and accurately by a geospatial query. Historically the Douglas-Peucker algorithm has been used for polygon simplification; however, due to the nature of the algorithm, a buffer must be added to the polygon before simplification to ensure pivotal points are not removed by the algorithm. This adds in additional error to the polygon simplification. ASDC’s goal is to test other methods of polyline simplification, such as Visvalingan-Whyatt and Opheim simplification alongside of Douglas-Peucker and different buffering methods, to produce less error during polygon simplification of TEMPO data swaths, and special spatial query geometries such as EPA non-attainment regions, and geopolitical boundaries.

Spatial Metadata↗
NASA NTRSDate not supplied

Use of Spatial Metadata Simplification for TEMPO

The National Aeronautics and Space Administration's (NASA) Atmospheric Science Data Center (ASDC) at NASA Langley Research Center in Hampton, VA provides atmospheric science data products and services to the science community, including enhanced search and subsetting capabilities for numerous Earth Science datasets. The ASDC is the official Distributed Active Archive Center (DAAC) of record for the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. TEMPO is situated on a geostationary satellite positioned at a longitude near the center of the conterminous United States and focused on North America, making hourly swaths of its field of regard from east to west. Spatial metadata is an essential component for the discovery and distribution of Earth Science data. The simplified polygonal boundaries representing the archived data files ensure that any granule can be identified quickly and accurately by a geospatial query. Historically the Douglas-Peucker algorithm has been used for polygon simplification; however, due to the nature of the algorithm, a buffer must be added to the polygon before simplification to ensure pivotal points are not removed by the algorithm. This adds in additional error to the polygon simplification. ASDC’s goal is to test other methods of polyline simplification, such as Visvalingan-Whyatt and Opheim simplification alongside of Douglas-Peucker and different buffering methods, to produce less error during polygon simplification of TEMPO data swaths, and special spatial query geometries such as EPA non-attainment regions, and geopolitical boundaries.

Spatial Metadata↗